Fault Detection Scheme Research Articles

Integrating condition-based monitoring with process sensors information for operation and maintenance in a functional modeling framework

Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the system.

The following article for this Special Issue was published in different Issue

International Journal of Robust and Nonlinear ControlVolume 33, Issue 7 RESEARCH ARTICLE The following article for this Special Issue was published in different Issue First published: 10 April 2023 https://doi.org/10.1002/rnc.6708AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Cheng C, Wang Q, Nikitin Y, Liu C, Zhou Y, Chen H. A data-driven distributed fault detection scheme based on subspace identification technique for dynamic systems. Int J Robust Nonlinear Control. 2023;33(5):3107-3128. doi: 10.1002/rnc.6554 https://onlinelibrary.wiley.com/doi/10.1002/rnc.6554 Volume33, Issue7Special Issue: Emerging Robust and Data‐driven Control Methods for Uncertain Learning Systems10 May 2023 RelatedInformation

Fault Detection and Zonal Protection Strategy of Multi-Voltage Level DC Grid Based on Fault Traveling Wave Characteristic Extraction

Currently, DC breakers are commonly used in mainstream protection schemes for DC grids to eliminate faults. However, the cost of high voltage DC (HVDC) breakers is high, and equipping each DC line with DC breakers is expensive. In order to minimize the number of DC breakers while ensuring the reliability of the power supply, a zonal protection strategy suitable for multi-voltage level DC grids is proposed. Subsequently, the qualitative impact of partial power interruption caused by fault DC areas is analyzed in the system. The basic zonal principle of the multi-voltage level DC grid is formulated, taking into account unbalanced power, the mode of system-level control, and the type of converters. Additionally, a time sequence coordination strategy is derived in detail based on the characteristics of DC breakers, AC/DC converters, DC/DC converters, AC breakers, high-speed switches, and other fault removal components. Finally, a seven-terminal DC grid is modeled in the PSCAD/EMTDC simulation platform. According to the simulation analysis, the DC grid can adopt the converter with fault clearing ability or an AC circuit breaker to cooperate with the fast disconnector (FD) to complete fault clearing in the DC fault area under the proposed zonal protection coordination strategy.

Intelligent Fault Detection for Rotating Machinery Using Cyclic Morphological Modulation Spectrum and Hierarchical Teager Permutation Entropy

Intelligent fault detection of rotating machines is essentially a pattern classification issue. At the same time, effectively obtaining fault features from the measured signals is a key step to timely diagnose the health status of rotating machinery and evaluate the results of fault classification. To accurately obtain effective fault information to enhance fault accuracy, this paper proposes a novel fault detection scheme based on cyclic morphological modulation spectrum (CMMS) and hierarchical Teager permutation entropy (HTPE). In this scheme, firstly, CMMS was developed to analyze the measured signal to obtain a series of CMMS slices with different frequency bands, which solved the deficiencies of manual empirical selection of frequency band bandwidth in the traditional cyclic modulation spectrum (CMS). Subsequently, by integrating Teager energy operator into hierarchical permutation entropy (HPE), an improved feature selection method named hierarchical Teager permutation entropy (HTPE) is presented to obtain fault information of different frequency band slices, which can improve the fault feature extraction capability of HPE. Finally, the acquired HTPE-based vectors are integrated into the extreme learning machine (ELM) classifier to achieve fault classification of rotating machinery under different working conditions. The proposed scheme is validated by experimental cases including cylindrical roller bearings and planetary gearboxes. The analysis results indicate that the proposed scheme not only can effectively obtain the fault features, but also accurately realize the classification and recognition of the fault mode. In addition, the proposed scheme can achieve higher detection accuracy than some existing algorithms.

Single fuzzy inference based fault detection and classification protection scheme for different types of short circuit faults on double circuit transmission lines

ABSTRACT An intelligent protection scheme based on the single fuzzy inference system and discrete Fourier transform is proposed for the faulty phase detection and classification of faults on the mutually coupled double circuit transmission lines. This proposed protection technique uses the magnitude of the pre-processed current information of the sending end bus only. This is implemented in the MATLAB/Simulink environment on a 400 kV, 50 Hz, and 300 km double circuit power system model. The efficacy of the proposed scheme has been tested by performing a wide range of simulation studies concerning different types of faults (common short circuit faults, cross-country and evolving faults, and high impedance faults) and typical fault scenarios (current transformer saturation, noisy environment, and fault during power swing conditions) with variations in different fault parameters and operating conditions. The results presented confirm that the proposed method detects/classifies all the faults within one cycle time and is reliable with a detection/classification accuracy of 99.75%. It is immune to variations in fault parameters, and different operating conditions, and is not affected by the zero sequence mutual impedance. It does not require any training and communication link. Further, the performance is also appraised with other training-based protection schemes.

An [formula omitted] optimization technique to distributed fault detection for multi-agent systems based on event-triggered mechanism

This paper proposes an H∞/H∞ optimization technique to distributed fault detection (FD) for multi-agent systems (MASs). To simultaneously consider the effects of unknown disturbances and faults in the MASs and event-triggered transmission errors (ETTEs) on the residual signal, a residual generator related to the event-triggered threshold coefficient is designed. The generated residuals achieve the best compromise between robustness to unknown disturbances and sensitivity to faults. The obtained results have a better detection performance by using H∞/H∞ optimization technique compared with other optimized schemes. In addition, the applicability of the proposed H∞/H∞ optimized distributed FD scheme is verified by the simulation of a vehicle lateral dynamic system.

An implementation of an enhanced DG primary control equipped with fault detection scheme

ABSTRACT This paper proposes an enhanced primary control unit of the distributed generator inside the microgrid when the DG is grid-connected and when operated autonomously. The control methods needed for the DG inverter when operated in grid-connected mode and autonomous operation mode have been investigated. The rotating reference frame employed for the control of the DG has been clarified to investigate how the active and reactive power of the DG are delivered to the main grid. When the DG suffers from local disturbance conditions, it should be disconnected from the main grid and switched off or its control unit is changed to voltage-frequency mode. Therefore, a fault detection scheme has been implemented to monitor the voltage and frequency of the DG bus. Hence, the performance of the proposed work has been validated under steady-state and short circuit conditions where the DG inverter is operated in grid-connected mode, switch-off mode, and autonomous mode.

DC fault detection in hybrid MTDC systems using transient average of DC reactor voltage

The existence of various types of converters makes the fault characteristics of the hybrid multi-terminal DC (MTDC) system more complicated than the voltage source converter-based (VSC-based) MTDC system. A reduced line commutated converter (LCC) model for DC fault analysis is first proposed in this paper, where the LCC-based rectifier with control effects is regarded as an equivalent RLC circuit. By neglecting the resistive and capacitive components in the high-frequency domain, the original network analysis is significantly simplified to derive concise time-domain expressions of the initial DC fault current. On top of this, a single-terminal DC fault detection scheme using the transient average of DC reactor voltage is designed for the hybrid MTDC system. The thresholds for detecting faults on the LCC-connected and VSC-connected lines are calculated based on the derived fault current expressions, respectively. Simulation studies based on PSCAD/EMTDC have validated the proposed fault analysis model is accurate within the initial period after fault, while the proposed fault detection scheme for hybrid MTDC systems is fast, sensitive, and tolerant to high fault resistances.

Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine

Timely and effective fault detection is essential to ensure the safe and reliable operation of wind turbines. However, due to the complex kinematic mechanisms and harsh working environments of wind turbine equipment, it is difficult to extract sensitive features and detect faults from acquired wind turbine signals. To address this challenge, a novel intelligent fault detection scheme for constant-speed wind turbines based on refined time-shifted multiscale fuzzy entropy (RTSMFE), supervised isometric mapping (SI), and adaptive chaotic Aquila optimization-based support vector machine (ACAOSVM) is proposed. In the first step, the RTSMFE method is used to fully extract features of the wind turbine system. The time-shifted coarse-grained construction technique and a refined computing technique are adopted in the RTSMFE method to enhance the capability of traditional multiscale fuzzy entropy for measuring the complexity of signals. Subsequently, an effective manifold learning approach, SI, is applied to obtain the important and low-dimensional feature set from the high-dimensional feature set. Finally, sensitive features are fed into the ACAOSVM classifier to identify faults. The proposed ACAO algorithm is used to optimize important parameters of the SVM, thereby improving its detection performance. Simulations and wind turbine experiments verified that the proposed RTSMFE outperforms existing entropy techniques in terms of complexity measurement and feature extraction. Furthermore, the proposed ACAOSVM classifier is superior to existing advanced classifiers for fault pattern recognition. Finally, the proposed intelligent fault detection scheme can more correctly and efficiently detect wind turbine single/hybrid faults than other recently published schemes.

Short-circuit fault detection scheme for DC microgrids on offshore platforms

Short-circuit fault detection scheme for DC microgrids on offshore platforms

A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process

In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression.

Fault detection and isolation for a secondary loop refrigeration system

Secondary loop refrigeration systems offer advantages over conventional system architectures: refrigerant charge reduction, maintenance simplification, and reduced safety hazards. Currently, no model-based fault detection for a secondary loop system has been reported. This article presents a fault detection and isolation scheme for a secondary loop refrigeration system where parametric faults (dirt/ice-buildup and secondary loop pump failure) and sensor faults (four temperature sensors in the secondary loop) are detected and isolated. The fault detection and isolation system is based on unknown input observers and on extended Kalman filters. The models are obtained through physical relations combined with experimental parameter determination (gray-box model) and are cross-validated against measurement data. The complete fault detection and isolation architecture is experimentally validated on a secondary loop refrigeration system mounted on a cooling box inside a climate chamber. Experimental results show that important parametric faults can be reliably detected and isolated, and the fault detection and isolation of sensor faults are effective.

Fault Diagnosis for Momentum Wheels of Communication Satellite Based on Artificial Neural Network

With the advent of several fault detection techniques in modern control systems design, this paper adopted the Artificial Neural Network (ANN) Fault Detection scheme for the Fault Detection of the Attitude Control System for a Communication Satellite. In satellite applications, telemetry data can be very large, and ANN is best suited for network modeling involving large sets of data. The availability of real satellite data from Nigcomsat-1R communication satellite provided a practical platform to assess the fault detection algorithm. Results obtained showed a good correlation between raw satellite telemetry data and Neural Network model-generated results for subsequent fault detection. The fault detection models were able to detect faults, log them and provide a notification to enhance subsequent isolation and rectification. Momentum Wheel Speed and Torque were used to investigate the performance of the wheels while the Momentum Wheel Voltage and Current helped to monitor the wheel’s health state. A fault is detected if the absolute difference between original output (MW Torque) and the NN Torque output is greater than 0.012. With this, an accuracy of 100% and mean squared error of 9.8489e-6 were achieved.

Estimating the Loss of Effectiveness of UAV Actuators in the Presence of Aerodynamic Effects

This letter proposes an approach for estimating the loss of effectiveness of UAV actuators while also considering the presence of aerodynamic effects, most specifically those regarding blade-flapping. The fault detection scheme is based on a Reduced Order Extended Kalman Filter Tolerant to Aerodynamic Effects (ROEKF-TAE) capable of detecting loss-of-effectiveness (LOE) faults in more than one actuator at the same time. The experimental results were performed using a Parrot Mambo micro drone and compared to those of a traditional EKF, as well as to those of a state-of-the-art adaptive Kalman Filter available in the literature. The filter proposed in this work could better distinguish between the effects of the actual fault and those of the blade-flapping disturbance when compared to the other filters. Also, it was able to identify with more precision which actuators were truly defective and which were not.

Novel Approach for Synchronous Generator Protection Using New Differential Component

A new stator fault detection and classification scheme for synchronous generators (SGs), based on an amplified discrete Teager-kaiser energy operator (ADTKEO) of a new differential component ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DC_SI</i> ), is proposed. Detection of an internal fault is based on the fulfillment of the ADTKEO ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ξ</i> ) with respect to the pre-determined threshold along with the checking of second-harmonic content in the current transformer (CT) secondary current. Afterward, the classification of internal faults is performed based on ‘ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ξ</i> ’. The performance of the proposed scheme is tested on various fault cases obtained from a simulation network developed in a real-time digital simulator and the developed laboratory prototype of the SG. The results indicate that the proposed scheme not only provides 100% winding coverage against phase-to-ground faults but is also capable of detecting other phase faults within a quarter cycle for low-resistance grounded SGs. At the same time, it stays extremely stable for external faults, even considering errors in CT along with its saturation (without compromising speed/sensitivity). The proposed scheme is independent of the sampling frequency, loading condition, decaying dc component present in the acquired signals, and rating of the SG.

A new wave-based fault detection scheme during power swing

A new wave-based fault detection scheme during power swing

Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems

Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS.

A high-impedance fault detection scheme for DC aircrafts based on comb filter and second derivative of voltage

Faults in a DC aircraft power system typically lead to serious equipment damage, which severely threatens the safety of the whole aircraft system. A fast and accurate real-time fault detection scheme is necessary for aircraft power systems to provide high reliability in the system. In this paper, a new fault detection device (FDD) is proposed based on the comb filter and second derivative of the system voltage to detect both low and high-impedance faults (HIFs) in a fast way. The proposed method utilizes the comb filter in the middle of the two first derivatives to detect both high and low-impedance faults within several microseconds. For demonstrating the efficiency, authenticity, and compatibility of the proposed method, digital time-domain simulations are carried out and verified by real-time simulations using an OPAL-RT simulator under different scenarios such as low- and high-impedance fault, overload, and motor starting to verify distinguishing between non-fault disturbances and faults. The results, which are compared with reported methods, prove the accuracy and speed of the proposed FDD in a DC aircraft.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Protection scheme for multi-terminal HVDC system with superconducting cables based on artificial intelligence algorithms

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme.